Honeybee: Locality-enhanced Projector

• The paper introduces a projector that flexibly manages visual tokens while preserving local spatial context to enhance multimodal understanding.
• It employs convolutional and deformable attention mechanisms to optimize spatial reasoning and throughput, achieving superior performance on benchmarks.
• Instruction-tuning on diverse multimodal datasets validates its robustness, outperforming conventional methods in vision-language tasks.

The Honeybee locality-enhanced projector is an architectural innovation for bridging vision encoders and LLMs within Multimodal LLMs (MLLMs). Its fundamental contributions are twofold: flexible management of visual token budget and structurally enforced preservation of local context from visual features, enabling sophisticated visual–language understanding while maintaining high computational efficiency. Honeybee's modular projector block is realized via convolutional (C-Abstractor) or deformable attention (D-Abstractor) mechanisms, surpassing previous projectors by optimizing spatial reasoning and throughput trade-offs (Cha et al., 2023). This approach, strengthened by a rigorously curated instruction-tuning regimen and validated through multi-benchmark evaluation, positions locality-preserving design as a central technology in advanced MLLMs.

1. System Architecture and Projector Design

Honeybee’s MLLM framework consists of a frozen vision encoder producing region features $X_{\text{feat}} \in \mathbb{R}^{N\times d_v}$, a trainable locality-enhanced projector generating $M$ visual tokens $X_{\text{img}} \in \mathbb{R}^{M\times d_l}$, and an autoregressive LLM consuming joint visual and text inputs. The projector block is parameterized for flexible output token count ($M$ can be set to 144, 256, 576, etc.), supporting variable fidelity/throughput for downstream LLM inference.

Honeybee Projector Variants

• C-Abstractor: Applies $L$ stacked ResNet bottleneck blocks for local feature transformation, followed by adaptive average pooling to map features into a $\sqrt{M}\times\sqrt{M}$ spatial grid and then additional $L$ ResNet blocks. Final flattening yields $M=d_l$ tokens. This ensures each token is influenced by spatially contiguous regions.
• D-Abstractor: Employs $M$ learnable queries, each initialized with a reference grid point on the feature map. Through $L$ deformable attention layers, queries sample local neighborhoods via learned offsets and attention weights, aggregating spatially relevant patches. The final token set is stitched from these spatially-aware aggregations.

Pseudocode Overview:

def Honeybee_Projector(X_feat, M, type='C-Abstractor'): if type == 'C-Abstractor': F = reshape_and_unflatten(X_feat) # H×W×d_v grid for l in range(L): F = ResNetBottleneckBlock(F) F_p = AdaptiveAvgPool(F, output_size=(sqrt(M), sqrt(M))) for l in range(L): F_p = ResNetBottleneckBlock(F_p) X_img = Flatten(F_p) elif type == 'D-Abstractor': Q = AdaptiveAvgPool(X_feat, output_size=M) P_ref = UniformGrid(H, W, M) for l in range(L): Δo, A = LinearProj(Q) X_img = [] for k in range(K): X_img += A[:, k] * Sample(X_feat, P_ref + Δo[:, k]) Q = LayerNorm(Q + X_img) return X_img

Flexible adaptive pooling and parameterized $M$ ensure efficiency and scalability (Cha et al., 2023).

2. Locality Preservation and Spatial Mechanisms

The projector’s locality property is essential for spatial understanding (e.g., relative object relationships). Purely linear or global attention-based abstraction typically collapses spatial structure, impairing fine-grained perception.

C-Abstractor Mechanism

Convolutional abstraction uses stacked local ResNet bottlenecks: $y_i = \sum_{j \in \mathcal{N}(i)} W_{i-j} x_j + b,$ where $\mathcal{N}(i)$ denotes the local receptive field (3×3 neighborhoods). Stacked $L$-layer convolution restricts each output’s dependence to an $L$-hop vicinity.

D-Abstractor Mechanism

Queries $z \in \mathbb{R}^{d_l}$ at reference points $p$ aggregate feature patches based on sampled offsets $\Delta o_k$ and attention weights $A_k$: $$z^{\ell+1} = \sum_{k=1}^K A_k^\ell \cdot X_{\text{feat}}(p + \Delta o_k^\ell),$$ with all parameters generated by linear projection from $z^\ell$.

Both methods maintain spatial relationships across tokens, which are subsequently integrated with $X_{\text{text}}$ in the LLM: $$p(Y | X_{\text{img}}, X_{\text{text}}) = \prod_i p(w_i | [X_{\text{img}}; X_{\text{text}}], w_{<i}),$$ training via cross-entropy.

3. Instruction-Tuning Regimen and Dataset Strategy

Training is structured in two phases:

• Pre-training (vision–language alignment): Projector is trained (vision encoder, LLM are frozen) on large-scale image–caption datasets (COYO100M, BlipCapFilt) with a next-token prediction objective for 200K steps.
• Instruction-tuning (multimodal task adaptation): Projector and LLM both train for 10K steps on a balanced mixture of six task groups, optimizing multimodal instruction following.

Dataset Mixture

A six-way mixture (∼214M total samples) includes:

• Captioning (COYO100M, BlipCapFilt)
• Open-ended VQA (VQAv2, GQA, OCRVQA, VSR)
• Multiple-choice VQA (ScienceQA, A-OKVQA)
• Referring expressions (RefCOCO, RefCOCO+, RefCOCOg, Visual Genome)
• Visual instructions (GPT-4 generated, LLaVA150K)
• Text-only instructions (ShareGPT)

Sampling uses per-dataset tuned ratios to avoid overfitting to smaller corpora and ensure broad coverage. Templates are fine-grained, one per dataset, and single-shot. VQA multi-turn concatenation and answer de-duplication mechanisms mitigate shortcut learning.

4. Empirical Evaluation and Benchmark Analysis

Honeybee’s performance is measured on four principal multimodal benchmarks:

• MME (perception split): 2,000 yes/no tasks
• MMBench: 1,800 multiple-choice items
• SEED-Bench: 900 image reasoning tasks
• LLaVA-Bench: subjective GPT-4 scoring (0–100 scale)

Normalized score: $$N = \frac{(\text{MME}/2000 + \text{MMBench}/1800 + \text{SEED}/900)}{3} \times 100$$

Selected Results:

Model	MMBench	MME	LLaVA	N	Benchmark Setting
Honeybee C-Abstr.	70.1	1891.3	67.1	71.6	Vicuna-7B, M=144, res=224
LLaVA-1.5	64.3	1510.7	63.4	63.4	SOTA 7B
Honeybee D-Abstr.	70.8	1835.5	66.3	—	Vicuna-7B, M=144, res=224
Honeybee (High Budget)	73.2	1944.0	75.7	—	M=256, Vicuna-13B, res=336

Efficiency:

Projector	Tokens	Pretrain Time (s/step)
Linear	256	3.04
Resampler	144	2.28
C-Abstractor	144	2.23

Honeybee achieves state-of-the-art accuracy with fewer visual tokens and lower runtime per training step (Cha et al., 2023).

5. Ablation Studies of Projector and Instructional Choices

Detailed ablations reveal central design factors:

(A) Spatial Understanding: For M=144, C-Abstractor yields N=53.5 vs. N=43.9 for the Resampler, confirming a >10-point gain from locality enhancement when token count is limited.

(B) Data Mixture: Removing instruction sets causes marked drops:

• Removing VQA-open: MME −256, MMBench −1.8, LLaVA −2.3
• Removing GPT-4 instructions: LLaVA −12.6

(C) Dataset Balancing: Per-dataset tuned ratios outperform all other balancing strategies.

(D) Template Granularity: Fine-grained, single template outperforms coarse or multi-template schemes.

(E) Multi-turn & De-duplication: These mechanisms modestly improve generalization (N=70.6 vs. N=69.6).

(F) Projector Architecture: C-Abstractor with ResNet bottlenecks extracts more locality than ConvNext or standard convolution; D-Abstractor benefits from reference-point initialization and v-pooled query. Linear and simple MLP projectors yield marginal or negative gains.

6. Comparative Perspective and Transferable Insights

Research on the Spatial-Aware Efficient Projector (SAEP) underscores the relevance of multi-layer feature aggregation, structured 2D inductive bias, separable convolution, and token reduction for locality preservation (Qian et al., 2024). SAEP aggregates $K$ layers from a ViT encoder and applies pointwise and depthwise convolutions with residual pooling, effecting a 75% token reduction while boosting spatial understanding.

Transferable design principles for locality-enhanced projectors:

• Multi-level feature fusion: Aggregates both low-level cues and high-level semantics.
• Structured spatial mapping: Treats serialized tokens as 2D spatial maps prior to reduction.
• Separable convolution and pooling: Combines channel fusion and locality modeling, critical for spatial detail retention.
• Token reduction via stride: Ensures uniform down-sampling without information collapse.

A plausible implication is that Honeybee's modular design could be extended by incorporating adaptive layer weighting and dynamic kernel sizing, as proposed in SAEP, to further optimize locality–efficiency trade-offs in future projector architectures (Qian et al., 2024).

7. Context and Impact

The paradigm shift toward locality-enhanced visual projectors in MLLMs, represented by Honeybee’s design, addresses historically neglected trade-offs between spatial fidelity and computational burden. By outperforming global and linear abstraction methods on spatially sensitive benchmarks while accelerating inference and reducing memory overhead, Honeybee redefines best practices for vision–language multimodal fusion. Its instruction-tuning protocol and architectural choices systematically avoid shortcut learning, facilitating reliable generalization across diverse multimodal tasks. Research on locality—whether via convolutional, deformable attention, or multi-layer aggregation modules—is likely foundational for next-generation MLLMs.

Relevant Papers:

• "Honeybee: Locality-enhanced Projector for Multimodal LLM" (Cha et al., 2023)
• "Spatial-Aware Efficient Projector for MLLMs via Multi-Layer Feature Aggregation" (Qian et al., 2024)