NExT-Chat: An LMM for Chat, Detection and Segmentation
The development of large language models (LLMs) has greatly advanced the
field of multimodal understanding, leading to the emergence of large multimodal
models (LMMs). In order to enhance the level of visual comprehension, recent
studies have equipped LMMs with region-level understanding capabilities by
representing object bounding box coordinates as a series of text sequences
(pixel2seq). In this paper, we introduce a novel paradigm for object location
modeling called pixel2emb method, where we ask the LMM to output the location
embeddings and then decoded by different decoders. This paradigm allows for
different location formats (such as bounding boxes and masks) to be used in
multimodal conversations Furthermore, this kind of embedding based location
modeling enables the utilization of existing practices in localization tasks,
such as detection and segmentation. In scenarios with limited resources, our
pixel2emb demonstrates superior performance compared to existing
state-of-the-art (SOTA) approaches in both the location input and output tasks
under fair comparison. Leveraging the proposed pixel2emb method, we train an
LMM named NExT-Chat and demonstrate its capability of handling multiple tasks
like visual grounding, region caption, and grounded reasoning.
654c49333e8861928003c7ab
Ao Zhang
admin_assigned
2023-11-09T09:48:38.110Z
649a9133225ce03f1c82e0b2
/avatars/f3702a46a0a310d0292dc690794e7cc9.svg
Ao Zhang
user
AoZhang
654c49333e8861928003c7ac
Liming Zhao
claimed_verified
2023-11-09T10:14:56.750Z
629867eb52ad3199b9fe964c
/avatars/54a79d6b9affec95f1244f45ff714137.svg
Liming
user
liming712
654c49333e8861928003c7ad
Chen-Wei Xie
654c49333e8861928003c7ae
Yun Zheng
654c49333e8861928003c7af
Wei Ji
654c49333e8861928003c7b0
Tat-Seng Chua
NExT-Chat: An LMM for Chat, Detection and Segmentation
The development of large language models (LLMs) has greatly advanced the
field of multimodal understanding, leading to the emergence of large multimodal
models (LMMs). In order to enhance the level of visual comprehension, recent
studies have equipped LMMs with region-level understanding capabilities by
representing object bounding box coordinates as a series of text sequences
(pixel2seq). In this paper, we introduce a novel paradigm for object location
modeling called pixel2emb method, where we ask the LMM to output the location
embeddings and then decoded by different decoders. This paradigm allows for
different location formats (such as bounding boxes and masks) to be used in
multimodal conversations Furthermore, this kind of embedding based location
modeling enables the utilization of existing practices in localization tasks,
such as detection and segmentation. In scenarios with limited resources, our
pixel2emb demonstrates superior performance compared to existing
state-of-the-art (SOTA) approaches in both the location input and output tasks
under fair comparison. Leveraging the proposed pixel2emb method, we train an
LMM named NExT-Chat and demonstrate its capability of handling multiple tasks
like visual grounding, region caption, and grounded reasoning.
The pixel2emb method enables large multimodal models to effectively handle object location tasks by outputting location embeddings, leading to superior performance in multimodal conversations and various visual processing tasks.
large language models (LLMs) large multimodal models (LMMs) region-level understanding bounding box coordinates pixel2seq pixel2emb location embeddings multimodal conversations object location modeling detection segmentation visual grounding region caption grounded reasoning