TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization (2103.14862v5)

Abstract: Weakly supervised object localization (WSOL) is a challenging problem when given image category labels but requires to learn object localization models. Optimizing a convolutional neural network (CNN) for classification tends to activate local discriminative regions while ignoring complete object extent, causing the partial activation issue. In this paper, we argue that partial activation is caused by the intrinsic characteristics of CNN, where the convolution operations produce local receptive fields and experience difficulty to capture long-range feature dependency among pixels. We introduce the token semantic coupled attention map (TS-CAM) to take full advantage of the self-attention mechanism in visual transformer for long-range dependency extraction. TS-CAM first splits an image into a sequence of patch tokens for spatial embedding, which produce attention maps of long-range visual dependency to avoid partial activation. TS-CAM then re-allocates category-related semantics for patch tokens, enabling each of them to be aware of object categories. TS-CAM finally couples the patch tokens with the semantic-agnostic attention map to achieve semantic-aware localization. Experiments on the ILSVRC/CUB-200-2011 datasets show that TS-CAM outperforms its CNN-CAM counterparts by 7.1%/27.1% for WSOL, achieving state-of-the-art performance.

• The paper introduces TS-CAM, a novel approach that overcomes CNN limitations by reallocating semantic information to every image patch token.
• The method couples semantic-aware maps with attention mechanisms to capture complete object extents in weakly supervised localization.
• Experimental results on ILSVRC and CUB datasets demonstrate significant performance gains, outperforming CNN-based methods by up to 27.1%.

Overview of TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization

The paper presents a novel approach titled "TS-CAM: Token Semantic Coupled Attention Map," addressing critical challenges in Weakly Supervised Object Localization (WSOL). WSOL is tasked with learning object localization models using only image-level annotations. Conventional convolutional neural networks (CNNs) suffer from the partial activation issue, where only local discriminative regions are highlighted, missing the full object extent. This paper argues that this limitation is intrinsic to CNNs due to their inability to capture long-range dependencies among features.

Methodology

The proposed TS-CAM leverages the power of the visual transformer’s self-attention mechanism to overcome this limitation. The method comprises several stages:

1. Image Tokenization: The image is decomposed into a sequence of patch tokens for spatial embedding. This step is foundational in enabling long-range dependency extraction inherent to the transformer architecture.
2. Semantic Re-allocation: Contrary to visual transformers, where the class token is the focus of semantic information, TS-CAM reallocates semantics to each patch token, allowing them to be aware of object categories.
3. Attention and Semantic Coupling: The final stage introduces a coupling of semantic-aware map derived from patch tokens with a semantic-agnostic attention map. This coupling produces comprehensive localization maps capable of identifying complete object extents.

Experimental Results

Using the ILSVRC and CUB-200-2011 datasets, TS-CAM achieves substantial improvements over previous CNN-based approaches. It specifically outperforms the CNN-CAM counterparts by 7.1% on ILSVRC and 27.1% on CUB, establishing a new baseline in WSOL tasks. The paper reveals the effectiveness of TS-CAM in providing more accurate and complete localization compared to existing methods.

Implications and Future Directions

The implications of TS-CAM are significant for both practical applications and theoretical advancements in computer vision. Practically, the ability to utilize transformers for localization tasks opens avenues for more robust object localization models that are less dependent on exhaustive annotations. Theoretically, it emphasizes the importance of long-range feature dependency in achieving comprehensive object understanding. The new insights in token and semantic coupling offer promising directions for future research in enhancing model interpretability and accuracy in WSOL tasks.

An exciting area for future exploration is the adaptation of TS-CAM in real-time applications where speed and accuracy are critical, as well as its extension to other weakly supervised learning domains. Further investigation into the scalability of such transformer-based methods may also provide insights into their deployment on large-scale datasets or resource-constrained environments.

In conclusion, the innovative approach of TS-CAM exhibits a promising intersection between vision transformers and WSOL, providing significant performance improvements and setting the stage for future advancements in weakly supervised learning paradigms.